建模不同文化背景下的仇恨言论子空间，提升跨文化检测效果
Seeing Hate Differently: Hate Subspace Modeling for Culture-Aware Hate Speech Detection
- 为个体构建文化相关的仇恨言论子空间，融合多种文化属性
- 在多个数据集上平均性能超越现有方法1.05%
- 适合需要跨文化敏感的社交媒体内容审核场景
仇恨言论检测虽已广泛研究，但现有方法常忽视现实复杂性：训练标签存在偏见，且不同文化背景下对仇恨言论的定义差异显著。本文首先分析数据稀疏、文化纠缠和标签模糊三大挑战。为此提出一种文化感知框架，构建个体的仇恨言论子空间。通过建模文化属性组合缓解数据稀疏问题；利用标签传播捕捉各组合的独特特征以应对文化纠缠与标签歧义。最终生成的个体化仇恨子空间可进一步提升分类性能。实验表明，该方法在所有指标上平均优于当前最优模型1.05%。
原文摘要 · Abstract (English)
Hate speech detection has been extensively studied, yet existing methods often overlook a real-world complexity: training labels are biased, and interpretations of what is considered hate vary across individuals with different cultural backgrounds. We first analyze these challenges, including data sparsity, cultural entanglement, and ambiguous labeling. To address them, we propose a culture-aware framework that constructs individuals' hate subspaces. To alleviate data sparsity, we model combinations of cultural attributes. For cultural entanglement and ambiguous labels, we use label propagation to capture distinctive features of each combination. Finally, individual hate subspaces, which in turn can further enhance classification performance. Experiments show our method outperforms state-of-the-art by 1.05\% on average across all metrics.
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